Last week, our very own Steve Ball attended the DAMA Australia-hosted “Metadata Harvest Festival” – a panel discussion featuring representatives from three leading data management vendors: Informatica, Precisely, and Aristotle Metadata.
The panel explored the challenges and solutions around automating the collection and curation of metadata to support business intelligence (BI) and data-driven decision making.
Key themes that emerged from the discussion
Data Provenance and the Growing Complexity of Metadata Management
As organisations rely on an increasing number of data sources to feed their BI and analytics initiatives, the task of tracking data provenance and lineage becomes exponentially more complex.
The panellists agreed that when working with a single, centralised data source, metadata management is relatively straightforward. But as the data landscape becomes more distributed, with data flowing in from a wide variety of internal and external sources, automating the collection and cataloguing of comprehensive metadata is critical – yet also extremely challenging.
The Role of AI in Metadata Automation
The panellists acknowledged the potential for AI and machine learning to help automate certain aspects of metadata harvesting and curation.
For example, AI models could be trained to automatically identify and classify metadata elements, track data lineage, and detect data quality issues.
However, they cautioned that AI is not a silver bullet. Human curation and oversight will continue to be essential, as current AI technologies are still not reliable or robust enough to fully trust their outputs, especially when dealing with sensitive business data and decisions.
The Limitations of AI in the Face of Policy and Regulatory Changes
Another key point raised was the challenge of keeping AI-powered metadata management systems up-to-date in the face of evolving policies, regulations, and compliance requirements.
When new laws or organisational policies are introduced, AI systems may struggle to adapt and apply the necessary changes to metadata definitions and data handling processes.
The panellists stressed that human experts will need to step in to manage these types of changes until the AI systems can be retrained and updated accordingly, leading to potential delays and latency.
Overall, the “Metadata Harvest Festival” provided valuable insights into the state of metadata management and the evolving role of AI in this critical discipline.
While automation and AI hold promise, the panellists emphasised that a human-centred, hybrid approach will be essential for organisations to effectively harness their data assets and derive meaningful business insights.
Any thoughts on these key themes? Join the conversation on Linkedin …